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of materials under pressure, with a view to identifying and formalizing rules and concepts; - Applying the DFT/MLIP “Crystal Structure Prediction CSP” methodology to various issues addressed by the research
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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approaches based on density functional theory (DFT) have been introduced in recent years. A new research theme, "organometallic structures (MOFs)," has been introduced more recently, proposing the use
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, electronic properties, and spectroscopic response. A major component of the project will focus on coupling DFT calculations with machine learning models to accelerate spectral prediction, identify robust
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metal dichaocogenides (TMDs) model the phononic bands of the host material with e.g. density functional theory (DFT) calculate the effect of a strain field on the electronic bands and the phonon modes
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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computational condensed matter physics. We seek a motivated researcher with expertise in density functional theory (DFT) to study emergent phenomena in quantum materials. Research Areas - Correlated electronic
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organic radicals as novel charge transfer systems for use in fuel cells, electrolysers and batteries. Knowledge of computational modelling of materials/molecules using density functional theory (DFT) based
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of chemically storing and releasing hydrogen, using methanol as a reservoir. Main activities: • Utilize global optimization codes and perform DFT calculations on supercomputers. • Analyze results and
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UnitedKingdomTime) Position Description: Apply Position Description We invite talented and motivated applicants for PhD and Postdoc positions in the group of Haowei Xu (https://www.haoweixu.com/ ) at the Department